{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "outputExpanded": false
   },
   "source": [
    "# Playing with new 2017 tf.contrib.seq2seq\n",
    "\n",
    "TF now has new `tf.contrib.seq2seq`. Let's make small example of using it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "outputExpanded": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.contrib.rnn import LSTMCell, GRUCell\n",
    "from model_new import Seq2SeqModel, train_on_copy_task\n",
    "import pandas as pd\n",
    "import helpers\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "outputExpanded": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "By this point implementations are quite long, so I put them in [model_new.py](model_new.py), while notebook will illustrate the application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch 0\n",
      "  minibatch loss: 2.302387237548828\n",
      "  sample 1:\n",
      "    enc input           > [9 5 9 0 0 0 0 0]\n",
      "    dec train predicted > [2 2 2 2 0 0 0 0 0]\n",
      "  sample 2:\n",
      "    enc input           > [2 8 5 9 7 7 6 0]\n",
      "    dec train predicted > [4 4 4 4 2 8 8 8 0]\n",
      "  sample 3:\n",
      "    enc input           > [9 9 5 5 0 0 0 0]\n",
      "    dec train predicted > [2 2 2 2 2 0 0 0 0]\n",
      "\n",
      "batch 1000\n",
      "  minibatch loss: 0.2620355188846588\n",
      "  sample 1:\n",
      "    enc input           > [4 3 8 7 9 9 3 3]\n",
      "    dec train predicted > [4 3 8 7 9 9 3 3 1]\n",
      "  sample 2:\n",
      "    enc input           > [2 4 8 8 2 6 5 0]\n",
      "    dec train predicted > [2 4 8 8 2 6 5 1 0]\n",
      "  sample 3:\n",
      "    enc input           > [7 2 7 7 9 0 0 0]\n",
      "    dec train predicted > [7 2 7 7 9 1 0 0 0]\n",
      "\n",
      "batch 2000\n",
      "  minibatch loss: 0.08898009359836578\n",
      "  sample 1:\n",
      "    enc input           > [6 3 4 6 5 7 5 2]\n",
      "    dec train predicted > [6 3 4 6 5 7 5 2 1]\n",
      "  sample 2:\n",
      "    enc input           > [4 6 4 0 0 0 0 0]\n",
      "    dec train predicted > [4 6 4 1 0 0 0 0 0]\n",
      "  sample 3:\n",
      "    enc input           > [9 5 7 6 0 0 0 0]\n",
      "    dec train predicted > [9 5 7 6 1 0 0 0 0]\n",
      "\n",
      "batch 3000\n",
      "  minibatch loss: 0.04030259698629379\n",
      "  sample 1:\n",
      "    enc input           > [8 3 6 5 2 0 0 0]\n",
      "    dec train predicted > [8 3 6 5 2 1 0 0 0]\n",
      "  sample 2:\n",
      "    enc input           > [4 2 9 3 0 0 0 0]\n",
      "    dec train predicted > [4 2 9 3 1 0 0 0 0]\n",
      "  sample 3:\n",
      "    enc input           > [7 6 6 0 0 0 0 0]\n",
      "    dec train predicted > [7 6 6 1 0 0 0 0 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "\n",
    "with tf.Session() as session:\n",
    "\n",
    "    # with bidirectional encoder, decoder state size should be\n",
    "    # 2x encoder state size\n",
    "    model = Seq2SeqModel(encoder_cell=LSTMCell(10),\n",
    "                         decoder_cell=LSTMCell(20), \n",
    "                         vocab_size=10,\n",
    "                         embedding_size=10,\n",
    "                         attention=True,\n",
    "                         bidirectional=True,\n",
    "                         debug=False)\n",
    "\n",
    "    session.run(tf.global_variables_initializer())\n",
    "\n",
    "    train_on_copy_task(session, model,\n",
    "                       length_from=3, length_to=8,\n",
    "                       vocab_lower=2, vocab_upper=10,\n",
    "                       batch_size=100,\n",
    "                       max_batches=3000,\n",
    "                       batches_in_epoch=1000,\n",
    "                       verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fun exercise, compare performance of different seq2seq variants.\n",
    "\n",
    "Comparison will be done using train loss tracks, since the task is algorithmic and data is generated directly from true distribution and out-of-sample testing doesn't really make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "loss_tracks = dict()\n",
    "\n",
    "def do_train(session, model):\n",
    "    return train_on_copy_task(session, model,\n",
    "                              length_from=3, length_to=8,\n",
    "                              vocab_lower=2, vocab_upper=10,\n",
    "                              batch_size=100,\n",
    "                              max_batches=5000,\n",
    "                              batches_in_epoch=1000,\n",
    "                              verbose=False)\n",
    "\n",
    "def make_model(**kwa):\n",
    "    args = dict(cell_class=LSTMCell,\n",
    "                num_units_encoder=10,\n",
    "                vocab_size=10,\n",
    "                embedding_size=10,\n",
    "                attention=False,\n",
    "                bidirectional=False,\n",
    "                debug=False)\n",
    "    args.update(kwa)\n",
    "    \n",
    "    cell_class = args.pop('cell_class')\n",
    "    \n",
    "    num_units_encoder = args.pop('num_units_encoder')\n",
    "    num_units_decoder = num_units_encoder\n",
    "    \n",
    "    if args['bidirectional']:\n",
    "        num_units_decoder *= 2\n",
    "    \n",
    "    args['encoder_cell'] = cell_class(num_units_encoder)\n",
    "    args['decoder_cell'] = cell_class(num_units_decoder)\n",
    "    \n",
    "    return Seq2SeqModel(**args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Test bidirectional/forward encoder, attention/no attention, in all combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f589d68>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAHVCAYAAACkMYdCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAoD/ByhlWCknKmguAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dbda550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=False, attention=False)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    loss_tracks['forward encoder, no attention'] = do_train(session, model)\n",
    "\n",
    "\n",
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=True, attention=False)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    loss_tracks['bidirectional encoder, no attention'] = do_train(session, model)\n",
    "\n",
    "\n",
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=False, attention=True)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    loss_tracks['forward encoder, with attention'] = do_train(session, model)\n",
    "\n",
    "    \n",
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=True, attention=True)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    loss_tracks['bidirectional encoder, with attention'] = do_train(session, model)\n",
    "\n",
    "pd.DataFrame(loss_tracks).plot(figsize=(13, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Naturally, attention helps a lot when the task is simply copying from inputs to outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Test GRU vs LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=True, attention=True, cell_class=LSTMCell)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    t0 = time.time()\n",
    "    lstm_track = do_train(session, model)\n",
    "    lstm_took = time.time() - t0\n",
    "\n",
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(1)\n",
    "with tf.Session() as session:\n",
    "    model = make_model(bidirectional=True, attention=True, cell_class=GRUCell)\n",
    "    session.run(tf.global_variables_initializer())\n",
    "    t0 = time.time()\n",
    "    gru_track = do_train(session, model)\n",
    "    gru_took = time.time() - t0\n",
    "    \n",
    "gru = pd.Series(gru_track, name='gru')\n",
    "lstm = pd.Series(lstm_track, name='lstm')\n",
    "tracks_batch = pd.DataFrame(dict(lstm=lstm, gru=gru))\n",
    "tracks_batch.index.name = 'batch'\n",
    "\n",
    "gru.index = gru.index / gru_took\n",
    "lstm.index = lstm.index / lstm_took\n",
    "tracks_time = pd.DataFrame(dict(lstm=lstm, gru=gru)).ffill()\n",
    "tracks_time.index.name = 'time (seconds)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ee59d68>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFNCAYAAAD/+D1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f3ace48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracks_batch.plot(figsize=(8, 5), title='GRU vs LSTM loss, batch-time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11eba1a20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFNCAYAAAA6iqfcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f97b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracks_time.plot(figsize=(8, 5), title='GRU vs LSTM loss, compute-time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "GRU has fewer parameters, so training supposed to be faster? This test doesn't show it."
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
